General public is not aware,
however, that slogans like “We need to teach students coding”, “Student have to
learn informatics” and similar, have been around since the begging of mass
production of personal computers, meaning, for about 30 years already.

And not only in America.

In Russia, for example, all university
students had to learn how to code using MS Basic and FORTRAN since yearly 1980s
(at least). Since late 1990s all high schools in Russia were
teaching informatics. Maybe, that is one of the reasons that the match between
the U.S. and Russian cyber forces looks like a draw (at least) despite the huge
technical and financial advantage of the U.S. (“Why Did Russian
Cyber Forces Beat Their U.S. Adversaries in 2016?”).

BTW: If everything goes
according to a plan, in the near future all Russian schools will also be
teaching game of Chess (it looks like in Russia they really want to force
everyone into thinking - what a dictatorship!).

America has never suffered
from having low numbers of American-born students selecting STEM related field,
including computer study, because the world always could provide enough
qualified foreigners wishing to work in the U.S. It has been just cheaper to
import foreign born professionals than preparing them domestically. This
situation, however, may be changing due to changing immigration policies (“What Would
Businesses Do if No Foreign Students Could Come In the Country Anymore?”)

This is one of the reasons for
reignited urgency for reviving 30-year old slogans. For example, on December
12, 2017, in his Testimony Before the U.S. Senate Committee on Science,
Commerce, and Transportation Subcommittee on Communications, Technology, Innovation
and the Internet, Vice President AI and IBM Q, Dr. Dario Gil said: “There are
actions we must take now to ensure the workforce is prepared to embrace the era
of AI … we must address the shortage of workers with the skills needed to make
advances in AI … We need to match skills education and training with the actual
skills that will be required in the emerging age of AI … We can use the example
of the adoption of software programming as a critical skill that is taught in
many high school and colleges. Some colleges require that all students learn
how to code since they consider it a necessary skill for success. Students
becoming proficient in programming have a wider range of job opportunities.” (https://www.commerce.senate.gov/public/_cache/files/492f7274-c35f-445e-85c5-de2ff9549f3c/A910A871CB1AEAD789BA779052DE21E2.gil-testimony.pdf).

various speakers confirmed
again and again the importance of training all U.S. students to
coding/informatics/data/cyber/computer skills (although, when asked in what
field will be the next breakthrough application of AI, no one named education!
Why? Because many AI professionals still have no definition of AI!).

Part II: Pedagogy of
cyber education

However, the current
pedagogical approach to advancing cyberlearning is based on an insufficient
methodology.

Coding is essentially matching
something, which was classified or identified with a code.

“Classified” or “identified”
usually means a set of actions which need to be performed in a specific order
under specific conditions, and is usually called “an algorithm”.

“A code” represents a set of
symbols, which represent specific operations over specific objects with
specific properties.

The process of coding is
essentially composed of two independent sub-processes: (1) development of the
algorithm; and (2) assigning a symbolic code (a command) to each element of the
algorithm.

General public usually makes
an equivalence between “coding” and “assigning a symbolic code to each element
of the algorithm”. As the result, “learning how to code” (and cyberlearning in
general) is shrank to “learning a code”, i.e. memorizing symbolic
representation of various commands.

In reality, memorizing
coding commands without being able to produce a workable algorithm is like
memorizing the meaning the individual words of a foreign language, but not
knowing the grammar, hence not being able to produce a meaningful sentence.

An ability to develop a
workable algorithm is
the central and the most important ability of coding, programming, and
computational thinking in general.

In order to be as efficient as
possible, the process of cyberlearning should be based on already developed
ability of developing workable algorithms.

That means, that the
development of algorithmic thinking should precede the development of
computational thinking.

Say a name of any device which
comes to mind.

A phone.

A TV set.

A gas station pump.

Any device!

They ALL – all devices
in the world – have been designed using algorithmic
thinking (and some coding, but not at the first stages of the
designing).

Every single technological
process – from the first assembly line to the Amazon warehouse and shipping
facility – also has been designed using algorithmic thinking.

Designing the launch protocol
for a shuttle or a rocket, or a recipe for a meal, or a plan for a wedding is
impossible without using

algorithmic thinking.

And, of course, every single
code written to operate any device or a process, from a TV remote control to
the blockchain technology, has been written using algorithmic thinking.

In general, designing a
device, or a protocol, or a process, or a program which includes a set of
actions which are distributed in time is impossible without
the use of algorithmic thinking.

Cyber
thinking represents a small part the algorithmic thinking and just cannot be
developed without having developed a sufficient level of the algorithmic
thinking.

But the development and
advancement of the algorithmic thinking does NOT require any reliance on
computer programming, and computers in general. In fact, learning how to code,
or write programs is NOT the best way to development of algorithmic thinking.

Algorithmic thinking can and
needs to be advanced outside (before!) of the computer, or programming, or
coding classes. This has to be done within a variety of STEM subjects. Two
subject fields, which are the most suited for advancing algorithmic thinking, are
physics and mathematics.

According to
the Wikipedia: “Computational Thinking is the thought processes involved in
formulating a problem and expressing its solution(s) in such a way that a
computer - human or machine - can effectively carry out. Computational Thinking
is an iterative process based on three stages: 1) Problem Formulation
(abstraction), 2) Solution Expression (automation), and 3) Solution Execution
& Evaluation (analyses)”.

Simply
saying, computational thinking has two parts: developing the solution of a
problem (a.k.a. thinking, or reasoning), and coding (translating into computer
operations) that solution using a language understandable by a computer.

The later part – coding – relies mostly on
memorizing lines of computer commands (or, if using a high-level
object-oriented programming – memorizing a set of programming operations).

Imagine that you want to learn
a foreign language, and you memorized the whole dictionary, so you can
translate – both ways – any individual word. You still will not be able to
read, or write, or talk, because you do not know how to compose a correct
sentence – for that you also need to know the grammar of the language (and to
practice). Exactly the same situation happens, if you learn all coding
commands, but cannot develop a correct algorithm which
represents the solution of a problem you need to solve.

That is why the first part of the definition of the computational thinking –
“formulating a problem and expressing its solution” – is the most important
part of the “ computational thinking” process.

And this is the part which is lacking in school education.

And this is the part, teaching of which requires the most of the effort of a
teacher.

And this is the part which represents the type of a scientific thinking, which
has a natural place and natural development when study physics (BTW: in
“computational thinking”, “scientific thinking, “critical thinking”, etc. the
most important part of a definition is “thinking”).

When learning how to solve a problem about how to walk a rope, and when
learning how to solve ANY physics problems, a student – under the guidance of
an experienced teacher – uses and develops his or her problem-solving
abilities, which have a universal nature, or
meta-nature (click here for more on what does it mean thinking as a physicist).

Everyone who learns physics (from a good teacher), automatically develops
the most important part of a computational thinking - thinking (!), and can
easily learn computer coding – the opposite is just not true (BTW: this
is “WHY all
students need to learn physics”).

Consistency demands to state
that the success in study physics, math, chemistry is impossible without preceding
success in reading, writing, arithmetic. Too often these days one can
read that students do not need to learn handwriting because they can type, or
students do not need to know multiplication table (just an example) because
they can use a calculator. People who make those statements don’t know anything
about leaning; and people who believe in those statements should stop
walking because they can drive a car - the same logic!

Once again, I need to stress
that the development of the advanced algorithmic thinking is impossible without
the development of thinking ability – in general. Expecting the development of
the advanced cyber or algorithmic thinking without making sure that the person
has the mental and intellectual capabilities required for that, is like
expecting that a person who can barely walk would win a Marathon.

Any type of thinking is
happening in the brain.

Advanced thinking requires advanced brain.

As I like to say (www.GoMars.xyz/6LT.html): “If the
only exercise students had been doing for twelve years is squats, they will not
be good at push-ups and pull-ups. Do not expect from students an ability to
think if all they had to do for twelve years was memorizing facts and rules”.

I think the following analogy
will be useful for IT professionals. The evolution of the growing brain due to
regular exercise is similar to the evolution of a CPU due to engineering
advances.

In
order to design an algorithm a person needs to be able to manipulate with a
large number of mental objects, mental entities (the complexity of the
algorithm is proportional to the required brain power).

That ability is based on
another fundamental human ability - imagination!
A general public is used to think that imagination is only important for
acting or writing. The fact is that one simply cannot succeed in any STEM
related field without having a developed imagination.

Every healthy person can toss
one ball and catch it again using just one hand. This ability is natural and
does not require any special training (again, for a healthy person).
And every healthy person can learn how to juggle with two or even three balls.
That would requires spending some time on practicing (a.k.a. effort), but there
are no any special restrictions on who can learn this skill.

In education, we have a very
similar situation regarding who can learn how to “juggle” with various mental
objects (abstracts, symbols, terms), and how many mental
objects somebody’s brain can handle at a time.

For example, understanding and
speaking simple sentences is a built -in natural ability, available to every
healthy person.

However, an ability to
manipulate with numbers or symbolic expressions needs to be trained (requires
more effort).

Every next level of
abstract thinking requires more advanced ability of manipulating with a larger
number of mental objects.

If to solve an equation or to
prove a theorem the logical chain of mental actions consists of twelve steps,
but a student starts forgetting the previous steps after the fifth,
that student will not be able to get the complete understanding of the related
material.

Learning abstract matters
however helps the development of the ability to manipulate with a large number
of mental objects in the same way like digging trenches helps to strengthen the
person 's body.

Imagine someone, who has never
been exercising, decided to take a 10-mile hike. After the first couple of
miles, one is completely out of breath, muscles are aching, the body is
shaking. After pondering for a while, the person decides to turn back home.

This illustration presents a
good analogy for what is happening to students who decided to go into a
STEM-related profession, but then change their major (according to Pew Research
Center

http://pewrsr.ch/2Dr2RxJ “Half of Americans think young
people don’t pursue STEM because it is too hard”).

The main reason for
dropping STEM-related majors is because schools have not prepared students’
brain for the required workload.

Two the most fundamental
issues of the contemporary education are:

1. insufficient professional
development of a large number of school teachers;

2. insufficient learnability
of school graduates.

Government agencies,
universities, charitable organization, startups and business accelerators are
talking the first issue, but evidently the strategy they use is not very
efficient (the efficient strategy is based on the advances of The Theory of Human Activity).

Unfortunately, currently there
is no government agency, a university, a charitable organization, a startup or
business accelerator which would address the issue of insufficient learnability of
school graduates.

Imagination needs to be
trained and developed. This type of training requires a special methodology and a
specific teaching technique (www.GoMars.xyz).

However, in order for physics,
mathematics, or any subject, to be an effective tool for the development and
advancement of algorithmic thinking, those subjects must be taught in a
specific way; i.e. the development of the algorithmic thinking should be one of
the specific objectives of the teacher teaching those subjects (ideally, all
teachers teaching STEM subjects).

Education has a direct effect
on security of a nation, including cyber security. More on that at

The state of public education has to be treated as a national security issue!

When someone stresses the importance of learning computer
coding, know this:all
intelligent people use a code – every day!

When we read, we decode
symbols (letters, words) into our internal meanings and feelings. When we
write, we code our internal meanings and feelings into symbols (if you add
algebra to reading and writing, you get another level of coding).

To demonstrate the importance
of using a correct sequence of steps to achieve a given goal (an important part
of any logical thinking), a teacher does not need to
teach how to code; a teacher can just offer a puzzle (for example, a mechanical one).

But everyone who is
thinking about teaching
computer coding to students who are not proficient enough in reading and
writing, should know:

“It will not work!”

If you are someone who is
still trying to do this, that might mean only two things.

Either you are an enthusiast
who does not know how people learn – in that case the right step would be
seeking an advice from a professional in teaching.

Or you are an imposter, who
does not really care about students and just uses the opportunity to gain
something personally beneficial (usually money).

Was Mr. Jeff Bezos a visionary, is
he still is, and will he remain to be such?

I don’t know about you, but since the Whole
Foods accusation by the Amazon my satisfaction with my shopping experience
dropped by about 20 %.

For example, I have two Whole Foods nearby,
and for two days in a row neither had such a simple product as organic rolled
oatmeal flakes. And the topinambur (a.k.a. sunchoke, item # 4791) is more often
soft than hard (which makes it useless).Is Mr. Jeff Bezos losing his Midas touch? Or
was he not so visionary in the first place?Don’t get me wrong, I have a deep respect to
Mr. Bezos; he just simply represents a very good “target” for any
business-related discussion.For example, did he already know in 1994 that
years later he would establish Amazon Prime, Amazon Studios, etc., etc.? Or he
was just growing one step at the time: “OK, this is done, let’s think, what can
we do now?”And if he was growing one step at the time,
does it mean he was not a visionary, or it means that his vision has also been
evolving together with him, or maybe it means something else?When he issued IPO and became a billionaire,
how much of that is due to his own talent, how much of that is due to his own
efforts, and how much of that due to other factors, like good timing, good
luck, good genes, good family, good schools, greed? The latter – greed – is related to the
actions of other people - the people who have decided (or helped to decide) how
many stocks to print, what price to set, and who and how much would also profit
from the mere fact of a stock offering.There is no way to measure the exact
percentage of the role of each factor in the final worth of a person. Maybe, there is no need for doing that.
Also, I would disagree with the last statement, I know that I would be in the
minority on this (but not alone! http://the3dforce.blogspot.com/2017/12/bincome.html).So, was Mr. Jeff Bezos a visionary, is he
still is, and will he remain to be such?With the available amount of the information,
there is no way to answer this question (or everyone can choose any answer one
likes).The results of his actions also depend on the
people closely working with him. But, on the other hand, this also depend on
how he selects those people – the quality which also changes in time.The next part of this discussion should be
focusing on questions like:- What is “a vision”?- What does it mean to be “a visionary”?- What are the signs, manifestations of a
“visionary person”?- Is being “a visionary” always good; and for
whom?- Is being “a visionary” comes from the
nature (genetic material), or the nurture (training, teaching); and in which
proportions (here we have a transition into the discussion about education)?- Does everybody need to be “a visionary”?
How many visionary persons (per 1000 people) is the best for a society?And more, and more important questions, but
that discussion requires a different time and place. BTW: Mr. Bezos, if you read this, I have a
personal favor to ask:https://youtu.be/jD-ZT7orr6QThank you! Appendix:

The history of mankind knows many large and even huge empires. They all are gone. They all are gone due to the same reason - the growth of an empire has led to such the size that the empire has become unmanageable. The small fluctuation in social conditions, management stiles, territorial goals, etc. have been growing to the level when the system could not sustain its whole any more. If Mr. Bezos has an ability to envision things far ahead, he should see that similar process started happening withing his empire. The number one indicator is the loss of the grip on the quality control of the products and processes. As an Amazon member from 2003, I see the growing number of examples when a U.S. merchant sells "Made in China" items, but the items are far from the description. Of course, I have no troubles with returning the items, but I would prefer not wasting my time on that. The largest empires in the history of the mankind did not survive their growth. It remains to be seen if the Amazon empire will be able to do better. BTW: it is happening with all retailers, not just Amazon: https://youtu.be/sK-tf5ROyds